Humans just can't thoroughly review the amounts of code AI can produce. But the answer is not YOLO and just ship blindly whatever AI created.
The answer is to move the human review/judgment upstream. You review specs. You review the steps. You review the verification rules. You review the contract the code must fulfill.
What's working, what’s still vaporware, who should be paying attention, and what the real path forward looks like for teams that want to be ready when orchestration goes mainstream.
Stanford researcher shares insights from large-scale studies on developer output, why early AI productivity claims were overstated, and what engineering leaders should (and shouldn’t) measure when rolling out AI across the software development lifecycle.
Four industry veterans — Kent Beck, Bryan Finster, Rahib Amin, and Punit Lad — shared their perspectives on how enterprises can adopt AI coding tools wisely.
We still need experts to recognize that it did a good job, though. It might produce something extraordinary with security problems, and we still need to be experts to recognize that.
Instead of adding yet another hot take on whether vibe coding is real or if AI is about to replace software engineers, I wanted to take a shot at predicting what software engineering might look like in 2027.
Code migrations are like cleaning the house. We know we have to do it, but we wish we didn't have to. Yet, we are still only using AI to create new code vs cleaning the existing mess.
That was the third time someone asked me that and it wasn’t even 11 AM on the first day of IUI, a research-focused conference about Intelligent User Interfaces.
Humans just can't thoroughly review the amounts of code AI can produce. But the answer is not YOLO and just ship blindly whatever AI created.
The answer is to move the human review/judgment upstream. You review specs. You review the steps. You review the verification rules. You review the contract the code must fulfill.
https://www.latent.space/p/reviews-dead